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Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:

1. In the reducer, the MapReduce API provides you with an iterator over Writable values. What does calling the next () method return?

A) It returns a reference to a Writable object from an object pool.
B) It returns a reference to the same Writable object each time, but populated with different data.
C) It returns a reference to the same Writable object if the next value is the same as the previous value, or a new Writable object otherwise.
D) It returns a reference to a different Writable object time.
E) It returns a reference to a Writable object. The API leaves unspecified whether this is a reused object or a new object.

2. You have a directory named jobdata in HDFS that contains four files: _first.txt, second.txt, .third.txt and #data.txt. How many files will be processed by the FileInputFormat.setInputPaths () command when it's given a path object representing this directory?

A) Four, all files will be processed
B) None, the directory cannot be named jobdata
C) Two, file names with a leading period or underscore are ignored
D) Three, the pound sign is an invalid character for HDFS file names
E) One, no special characters can prefix the name of an input file

3. All keys used for intermediate output from mappers must:

A) Implement a splittable compression algorithm.
B) Implement WritableComparable.
C) Implement a comparator for speedy sorting.
D) Be a subclass of FileInputFormat.
E) Override isSplitable.

4. What is the disadvantage of using multiple reducers with the default HashPartitioner and distributing your workload across you cluster?

A) You will not be able to compress the intermediate data.
B) By using multiple reducers with the default HashPartitioner, output files may not be in globally sorted order.
C) You will longer be able to take advantage of a Combiner.
D) There are no concerns with this approach. It is always advisable to use multiple reduces.

5. In a MapReduce job, you want each of your input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?

A) Increase the parameter that controls minimum split size in the job configuration.
B) Write a custom FileInputFormat and override the method isSplitable to always return false.
C) Set the number of mappers equal to the number of input files you want to process.
D) Write a custom MapRunner that iterates over all key-value pairs in the entire file.

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